How much memory does GLM-5.3 need?

About 426 GB at 4-bit, 803 GB at 8-bit and 1,509 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.

Can GLM-5.3 run on a 24 GB graphics card?

Not entirely: even at 4-bit it needs about 426 GB, so part of it would sit in system memory, which is much slower.

Is GLM-5.3 free for commercial use?

Its card lists a custom licence (glm-5.3), which sets its own terms; read it before any commercial use. Read the licence itself before relying on this summary; it is not legal advice.

What is GLM-5.3’s context window?

Its configuration sets 1,048,576 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.

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ANVE

Run it on your own hardware · updated 2026-10-16

GLM-5.3
How much memory it needs, what its licence allows, and whether it fits an office box.

Short answer. GLM-5.3 needs about 426 GB of memory at 4-bit, 803 GB at 8-bit and 1,509 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 753.3 billion parameters and a 1,048,576-token context window. Licence in short: custom licence: check before commercial use. By memory alone, on ANVE’s launch configurations: ANVE One does not fit, ANVE Office does not fit, ANVE Office Pro (GPU) does not fit. These are estimates from its published size, not benchmarks.

What it is

GLM-5.3 is a language model for chat, drafting and question answering, published by Z.ai (Zhipu) on Hugging Face on 25 August 2026. It has about 753.3 billion parameters and a configured context window of 1,048,576 tokens. It is a mixture-of-experts model: 256 experts, 8 used for each token.

Its card lists the licence as a custom licence (glm-5.3). In short, the licence sets its own terms; read it before any commercial use.

Hugging Face counted about 1,507,787 downloads of it in the 30 days before we checked (7 Oct 2026), and 2,126 people have liked it there.

The card lists 2 languages: en, zh (ISO codes as given on the card).

How much memory it needs

We size it the way you would size any model: the weights (parameters × bits ÷ 8), the KV cache for 8,192 tokens of context, and roughly 1.5 GB for the runtime itself. Real use varies with settings.

Each token of context adds about 0.09 MB of KV cache at 16-bit, so 32,768 tokens would add about 2.9 GB. This model uses multi-head latent attention, which stores a compressed cache.

Precision	Weights	KV cache (8,192 tokens)	Total, estimated
BF16 (full precision)	1,507 GB	0.74 GB	1,509 GB
8-bit (Q8_0)	800 GB	0.74 GB	803 GB
4-bit (Q4_K)	424 GB	0.74 GB	426 GB

Architecture at a glance

Setting	Value in config.json
Architecture class	GlmMoeDsaForCausalLM
Model type	glm_moe_dsa
Layers	78
Hidden size	6,144
Attention heads	64
Key-value heads	64
Head size	192
Vocabulary	154,880
Experts	256, 8 active per token
Configured context	1,048,576
Stored precision	bfloat16

Will it fit on ANVE?

These verdicts are arithmetic, not tests, against the specification at launch: ANVE One can give a model about three quarters of its 64 GB of unified memory, while ANVE Office and ANVE Office Pro hold it on a 24 GB graphics card and keep the rest of the stack in 64 GB or 128 GB of system memory. ANVE’s tested default is Qwen 3.8 27B; this model is not yet validated on ANVE.

Configuration (at launch)	Memory for the model	Verdict
ANVE One	about 48 GB of 64 GB unified memory	does not fit
ANVE Office	24 GB on the RTX PRO 4000 Blackwell SFF Edition	does not fit
ANVE Office Pro (GPU)	24 GB on the RTX PRO 4000 Blackwell	does not fit

What an office would use it for

This is frontier-scale: even at 4-bit it needs far more memory than any single office box, so it belongs on a multi-GPU server or a cloud service, not on an appliance.

Variants with the same size

GLM-5.3: a variant with the same size and memory needs (same parameter count, so the memory figures above apply).

Similar models to compare

DeepSeek-V3.2 (685.4B parameters, about 387 GB at 4-bit before context; commercial use allowed)
Nemotron-3-Ultra-550B-A55B (560.5B parameters, about 317 GB at 4-bit before context; custom licence: check before commercial use)
MiMo-V2.5-Pro (1023.2B parameters, about 577 GB at 4-bit before context; commercial use allowed)

Questions people ask

How much memory does GLM-5.3 need?: About 426 GB at 4-bit, 803 GB at 8-bit and 1,509 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can GLM-5.3 run on a 24 GB graphics card?: Not entirely: even at 4-bit it needs about 426 GB, so part of it would sit in system memory, which is much slower.
Is GLM-5.3 free for commercial use?: Its card lists a custom licence (glm-5.3), which sets its own terms; read it before any commercial use. Read the licence itself before relying on this summary; it is not legal advice.
What is GLM-5.3’s context window?: Its configuration sets 1,048,576 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.

What is not real yet

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.

GLM-5.3How much memory it needs, what its licence allows, and whether it fits an office box.